Risk assessment between aircrafts

ABSTRACT

A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a first aircraft. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.

FIELD OF THE INVENTION

The present invention relates to a method and associated system forperforming a risk assessment process associated with cohorts locatedwithin a plurality of aircrafts.

BACKGROUND OF THE INVENTION

Determining individuals capable of performing activities that may pose arisk to specified objects and locations typically comprises aninaccurate process with little flexibility. Accordingly, there exists aneed in the art to overcome at least some of the deficiencies andlimitations described herein above.

SUMMARY OF THE INVENTION

The present invention provides a risk assessment method comprising:

receiving, by an inference engine within a computing system, firstsensor cohort data associated with a first cohort, said first cohortlocated within a first aircraft;

receiving, by said inference engine, first group technology inferencesassociated with said first cohort;

generating, by said inference engine, first risk cohort inferences, saidgenerating said first risk cohort inferences based on said first grouptechnology inferences and said first sensor cohort data;

receiving, by said inference engine, first inference data generated bysaid inference engine, said first inference data comprising a firstplurality of inferences associated with said first cohort and a securityperimeter area surrounding an airport;

receiving, by said inference engine, second inference data generated bysaid inference engine, said second inference data comprising a second ofplurality of inferences associated with said first cohort and a pre/postsecurity area within said airport;

receiving, by said inference engine, third inference data generated bysaid inference engine, said third inference data comprising a third ofplurality of inferences associated with said first cohort and a gatearea within said airport;

receiving, by said inference engine, fourth inference data generated bysaid inference engine, said fourth inference data comprising a fourth ofplurality of inferences associated with said first cohort and a secondaircraft;

receiving, by said inference engine, fifth inference data generated bysaid inference engine, said fifth inference data comprising a fifth ofplurality of inferences associated with said first cohort and said firstaircraft;

generating, by said inference engine, sixth inference data, said sixthinference data comprising a sixth plurality of inferences associatedwith said first cohort and said first aircraft, wherein said generatingsaid sixth inference data is based on said first risk cohort inferences,said first inference data, said second inference data, said thirdinference data, said fourth inference data, and said fifth inferencedata;

generating, by said inference engine based on said sixth inference data,a first associated risk level score for said first cohort; and

storing, by said computing system, said sixth inference data and saidfirst associated risk level score.

The present invention provides a computing system comprising a processorcoupled to a computer-readable memory unit, said memory unit comprisinginstructions that when executed by the processor implement a riskassessment method, said method comprising:

receiving, by an inference engine within said computing system, firstsensor cohort data associated with a first cohort, said first cohortlocated within a first aircraft;

receiving, by said inference engine, first group technology inferencesassociated with said first cohort;

generating, by said inference engine, first risk cohort inferences, saidgenerating said first risk cohort inferences based on said first grouptechnology inferences and said first sensor cohort data;

receiving, by said inference engine, first inference data generated bysaid inference engine, said first inference data comprising a firstplurality of inferences associated with said first cohort and a securityperimeter area surrounding an airport;

receiving, by said inference engine, second inference data generated bysaid inference engine, said second inference data comprising a second ofplurality of inferences associated with said first cohort and a pre/postsecurity area within said airport;

receiving, by said inference engine, third inference data generated bysaid inference engine, said third inference data comprising a third ofplurality of inferences associated with said first cohort and a gatearea within said airport;

receiving, by said inference engine, fourth inference data generated bysaid inference engine, said fourth inference data comprising a fourth ofplurality of inferences associated with said first cohort and a secondaircraft;

receiving, by said inference engine, fifth inference data generated bysaid inference engine, said fifth inference data comprising a fifth ofplurality of inferences associated with said first cohort and firstaircraft;

generating, by said inference engine, sixth inference data, said sixthinference data comprising a sixth plurality of inferences associatedwith said first cohort and said first aircraft, wherein said generatingsaid sixth inference data is based on said first risk cohort inferences,said first inference data, said second inference data, said thirdinference data, said fourth inference data, and said fifth inferencedata;

generating, by said inference engine based on said sixth inference data,a first associated risk level score for said first cohort; and

storing, by said computing system, said sixth inference data and saidfirst associated risk level score.

The present invention advantageously provides a simple method andassociated system capable of determining individuals capable ofperforming activities that may pose a risk to specified objects andlocations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for performing a risk assessment processassociated with air travel, in accordance with embodiments of thepresent invention.

FIG. 2 illustrates a flowchart describing an algorithm used by thesystem of FIG. 1 for performing a risk assessment process associatedwith air travel, in accordance with embodiments of the presentinvention.

FIG. 3 illustrates a flowchart describing an algorithm used by thesystem of FIG. 1 for establishing a database adapted to establish aprobability of an inference based on data contained in the database, inaccordance with embodiments of the present invention.

FIG. 4 illustrates a flowchart describing an algorithm used by thesystem 2 of FIG. 1 for executing of a first query in a database toestablish a probability of an inference based on data contained in thedatabase, in accordance with embodiments of the present invention.

FIGS. 5A and 5B illustrate a flowchart describing an algorithm used bythe system of FIG. 1 for executing a second query in a database toestablish a probability of an inference based on data contained in thedatabase, in accordance with embodiments of the present invention.

FIG. 6 illustrates a computer apparatus used for performing a riskassessment process associated with air travel, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a system 2 for performing a risk assessment processassociated with air travel, in accordance with embodiments of thepresent invention. The risk assessment process is performed with respectto a plurality of cohorts located in an area governed by an airport(s).A cohort is defined herein as a group (or set) of things or peoplesharing (or potentially sharing) one or more characteristics.Characteristics may comprise, inter alia, physical characteristics,presence or absence of a condition(s), ages, geographical location, etc.A cohort may comprise any size group or set. For example, a cohort maycomprise a null set, an infinite set, or anything in between. Cohortsmay be generated by computing system 11 using the followingcategorizations:

1. Known Individuals:

A. In close proximity to each other at a same time.

B. Heading to a same aircraft.

C. Traveling to a same destination.

D. Changing planes at a same way point in temporal proximity.

2. Behavior Patterns:

A. Groups of nervous people.

B. Groups of people devoid of natural movement (e.g., controlledbreathing, flat effect, no emotion, etc).

C. Groups of natural clusters of people that avoid each other.

3. Sensor Patterns:

A. Traffic analysis.

B. Cluster of higher than normal signatures (e.g., nitrates).

C. Clusters of complementary chemical signatures.

The risk assessment process is performed to determine if any person orgroup of people pose a possible risk to perform nefarious activities(intentional or unintentional) associated with air travel (e.g., pose apossible hijacking threat for an aircraft). System 2 is used to performthe following steps:

1. Continuously retrieve data simultaneously from multiple sources(e.g., from multiple sensors, databases, etc). The data is associatedwith cohorts that pose a possible threat risk associated with airtravel.

2. Analyze the data using statistical and inference processing (i.e.,using rule sets 23).

3. Generate an output (e.g., risk assessment scores for the cohorts,alerts associated with the cohorts, etc) based on the analysis of thedata.

4. Generate responsive actions (i.e., based on the output from step 3)to be taken to avoid a possible risk associated with air travel.

5. Transmit the output from step 3 and the responsive actions from step4 to the proper authorities.

System 2 of FIG. 1 comprises sensors 10A, 10B, 10C, 10D, and 10E anddata retrieval/storage systems 5 connected to a computing system 11through a network 7. Sensors 10A, 10B, 10C, 10D, and 10E are connectedto network 7 through a data acquisition computer 8. Network 7 maycomprise any type of network including, inter alia, a local areanetwork, (LAN), a wide area network (WAN), the Internet, etc. Computingsystem 11 may comprise any type of computing system(s) including, interalia, a personal computer (PC), a server computer, a database computer,etc. Computing system 11 may comprise, inter alia, a services orientedarchitecture system, an independent Web 2.0 application/mashup system,etc. Computing system 11 comprises a memory system 14 (e.g., adatabase). Memory system 14 comprises an inference engine 18 (e.g., asoftware application), rule sets 31, inferences 27, risk level scores28, and alerts/responsive actions 25. Rule sets 31 are defined herein aspluggable sets of logic used to handle data and generate inferences 27and risk level scores 28. System 2 may comprise an optional accessterminal 9 connected to computing system 11. Optional access terminal 9may be used to access inference engine 18 (e.g., for maintenance) orrule sets 31, inferences 27, risk level scores 28, and alerts/responsiveactions 25. Inference engine 18 controls all functions related to:

1. Retrieving sensor cohort data from sensors 10A, 10B, 10C, 10D, and10E (i.e., via data acquisition computer), and additional data from dataretrieval/storage systems 5.

2. Generating inferences based on the sensor cohort data and theadditional data.

3. Generating risk level scores 28 for cohorts based on the inferences.

4. Generating alerts/responsive actions 25.

5. Transmitting risk level scores 28 and alerts/responsive actions 25 tothe proper authorities. Proper authorities may include, inter alia,police, military personnel, air traffic controller, aircraft pilots,airport personnel, etc.

6. Modifying risk level scores and transponder codes for aircraft 40.

Inference engine 18 performs the following process with respect tocohorts located in an aircraft 40A (or alternatively an aircraft 40B):

1. Audio, video, biometrical, olfactory, and sensory/actuator basedcohorts are used as input into inference engine 18.

2. Inference engine 18 may additionally receive group technology data.Group technology data is defined herein as inferences generated based oncharacteristics associated with a group of people. For example, if agroup of people comprises 3 children, a mom, and a dad, the grouptechnology data may comprise inferences that this group is a family.3. Inference engine 18 receives inferences associated with a cohort(s)in various locations.4. Inference engine 18 generates inferences based on all cohorts anddata inputted.5. Inference engine 18 computes associated risk level scores for acohort based on all generated inferences. These inferences and scoresare used to build an iterative portfolio of activities of interest anduse this to compare to observed cohort behavior within a system or groupof systems.6. Inference engine 18 generating alerts/responsive actions associatedwith cohorts and associated risk level scores.7. The process is recursive.

Sensors 10A are located in a security perimeter area 12 surrounding anairport 15. Security perimeter area 12 is defined herein as ageographical area that surrounds airport 15. Security perimeter area 12extends about 2 miles in any direction (i.e., at ground level, in theair, underground, etc) around airport 12. Sensors 10A may be locatedanywhere within security perimeter area 12. Sensors 10A may include anytype of sensors including, inter alia, audio sensors, video sensors,multi spectrum sensors (IR, UV, etc), chemical sensors, physical sensors(e.g., weight detection), intercept sensors (phone intercept devices,text message intercept devices, Internet intercept devices, etc), etc.

Sensors 10B are located in pre/post security area 15A within airport 15.Pre/post security area 15A is defined herein as an area that comprisesall locations within airport 15 except for a gate area (i.e., gate areasinclude: gates that are used to exit airport 15 for boarding airplanesand waiting area locations for the gate areas). Pre/post security area15A may include the following areas in airport 15:

1. Ticket counters for purchasing airline tickets.

2. Luggage retrieval areas.

3. Car rental counters (i.e., for renting automobiles)

4. Restrooms.

5. Restaurants and retail stores.

6. Security check point locations (e.g., metal detection areas, etc).

Sensors 10B may be located anywhere within pre/post security area 15A.Sensors 10B may include any type of sensors including, inter alia, audiosensors, video sensors, multi spectrum sensors (IR, UV, etc), chemicalsensors, physical sensors (e.g., weight detection), intercept sensors(phone intercept devices, text message intercept devices, Internetintercept devices, etc), etc.

Sensors 10C are located in gate area 15B within airport 15. Gate area15B is defined herein as an area that comprises locations within airport15 (or within airport 15 jurisdiction) that are external to pre/postsecurity area 15A. Gate area 15B may include, inter alia, gates (e.g.,doors or passageways) that are used to exit airport 15 for boardingairplanes, locations between gates and an aircraft (e.g., a walkwayconnecting a gate to an aircraft), waiting area locations for the gateareas, locations existing between food service providers for an aircraftand the aircraft, locations existing between luggage areas for placingluggage on or retrieving luggage from an aircraft and the aircraft.Sensors 10C may be located anywhere within gate area 15B. Sensors 10Cmay include any type of sensors including, inter alia, audio sensors,video sensors, multi spectrum sensors (IR, UV, etc), chemical sensors,physical sensors (e.g., weight detection), intercept sensors (phoneintercept devices, text message intercept devices, Internet interceptdevices, etc), etc.

Sensors 10D are located within aircraft 40A. Although FIG. 1 illustratesaircraft 40A located within security perimeter area 12 (e.g., waitingfor boarding passengers at gate area 15B, on a runway for takeoff, inthe air just after takeoff, etc), note that aircraft 40A may be locatedanywhere (e.g., in the air anywhere between airport 15 and anotherairport, at another airport, etc). Sensors 10D may be located anywherewithin aircraft 40A. Sensors 10D may include any type of sensorsincluding, inter alia, audio sensors, video sensors, multi spectrumsensors (IR, UV, etc), chemical sensors, physical sensors (e.g., weightdetection), intercept sensors (phone intercept devices, text messageintercept devices, Internet intercept devices, etc), etc.

Aircraft 40A comprises an optional computing system 11A. Computingsystem 40A comprises an inference engine 18A and optional rule sets.Computing system 11A comprises a private closed loop system capable ofgenerating private cohorts (i.e., internal cohorts generated from onlydata obtained from sensors 10D internal to aircraft 40A) and inferencesfrom sensor data retrieved from sensors 10D. Computing system 11A may beused in combination with computing system 11 (and computing system 11Bas described, infra) to generate combination cohorts, inferences, risklevel scores, alerts/responsive actions, and transponder code changesfor use by airports, aircrafts, etc. Alternatively, computing system 11Amay be used as a closed loop system to generate cohorts, inferences,risk level scores, alerts/responsive actions, and transponder codechanges independent from computing system 11 (and computing system 11Bas described, infra) in a case where aircraft 40A has lost allcommunications from computing system 11.

Sensors 10E are located within aircraft 40B. Although FIG. 1 illustratesaircraft 40B located external to security perimeter area 12 (e.g., inthe air anywhere between airport 15 and another airport, at anotherairport, etc), note that aircraft 40B may be located within securityperimeter area 12 (e.g., waiting for boarding passengers at gate area15B, on a runway for takeoff, in the air just after takeoff, etc).Sensors 10E may be located anywhere within aircraft 40B. Sensors 10E mayinclude any type of sensors including, inter alia, audio sensors, videosensors, multi spectrum sensors (IR, UV, etc), chemical sensors,physical sensors (e.g., weight detection), intercept sensors (phoneintercept devices, text message intercept devices, Internet interceptdevices, etc), etc.

Aircraft 40B comprises an optional computing system 11B. Computingsystem 40B comprises an inference engine 18B and optional rule sets.Computing system 11B comprises a private closed loop system capable ofgenerating private cohorts (i.e., internal cohorts generated from onlydata obtained from sensors 10E internal to aircraft 40B) and inferencesfrom sensor data retrieved from sensors 10E. Computing system 11B may beused in combination with computing system 11 (and computing system 11Aas described, supra) to generate combination cohorts, inferences, risklevel scores, alerts/responsive actions, and transponder code changesfor use by airports, aircrafts, etc. Alternatively, computing system 11Bmay be used as a closed loop system to generate cohorts, inferences,risk level scores, alerts/responsive actions, and transponder codechanges independent from computing system 11 (and computing system 11Aas described, supra) in a case where aircraft 40B has lost allcommunications from computing system 11.

Computing system 11 generates various public cohorts (i.e., for inputsto inference engine 18) based on data from sensors 10A, 10B, 10C, 10D,and 10E. Computing system 11A generates various private cohorts (i.e.,for inputs to inference engine 18A) based on data from sensors 10D.Computing system 11B generates various private cohorts (i.e., for inputsto inference engine 18B) based on data from sensors 10E. Examples ofcohorts (public and private) that may be generated may include, interalia:

1. Retina detection cohort (e.g., pupil dilation(s), etc).

2. Furtive glance cohort (e.g., normal looking around vs. rapidbidirectional viewing).

3. Respiration cohort (e.g., respiration monitoring and detection).

4. Perspiration cohort (e.g., detection of any abnormal perspirationthat may be occurring). Additionally, a perspiration cohort may comprisedetection of a possible contagious person within aircraft 40A or 40B(e.g., flu, etc).

5. Pallor cohort (e.g., a reduced amount of oxyhemoglobin in skin ormucous membrane, a pale color which may be caused by illness, emotionalshock or stress, avoiding excessive exposure to sunlight, anemia, orgenetics). A reduced amount of oxyhemoglobin is more evident on the faceand palms.6. Facial recognition cohorts (e.g., visually measuring facial stress,etc).

As a cohort(s) (e.g., comprising a person or people) enters securityperimeter area 12 (i.e., from a location external to security perimeterarea 12), sensors 10A immediately begin to monitor the cohort(s) (e.g.,using video monitors, audio monitors, etc). The cohort(s) iscontinuously monitored while they are within security perimeter area 12.Sensors 10 generate first monitoring data associated with the cohort(s)and security perimeter area 12. The first monitoring data is retrievedby data acquisition computer 8. Data acquisition computer 8 transmitsthe first monitoring data to computing system 11.

As the cohort(s) (e.g., comprising a person or people) exits securityperimeter area 12 and enters pre/post security area 15A, sensors 10Bimmediately begin to monitor the cohort(s) (e.g., using video monitors,audio monitors, etc). The cohort(s) is continuously monitored while theyare within pre/post security area 15A. Sensors 10B generate secondmonitoring data associated with the cohort(s) and pre/post security area15A. The second monitoring data is retrieved by data acquisitioncomputer 8. Data acquisition computer 8 transmits the second monitoringdata to computing system 11.

As the cohort(s) (e.g., comprising a person or people) exits pre/postsecurity area 15A and enters gate area 15B, sensors 10C immediatelybegin to monitor the cohort(s) (e.g., using video monitors, audiomonitors, etc). The cohort(s) is continuously monitored while they arewithin gate area 15B. Sensors 10C generate third monitoring dataassociated with the cohort(s) and gate area 15B. The third monitoringdata is retrieved by data acquisition computer 8. Data acquisitioncomputer 8 transmits the third monitoring data to computing system 11.

As the cohort(s) (e.g., comprising a person or people) exits gate area15B and enters aircraft 40A (e.g., an airplane, a helicopter, etc),sensors 10D immediately begin to monitor the cohort(s) (e.g., usingvideo monitors, audio monitors, etc). The cohort(s) is continuouslymonitored while they are within aircraft 40. Sensors 10D generate fourthmonitoring data associated with the cohort(s) and aircraft 40. Thefourth monitoring data is retrieved by data acquisition computer 8 (andoptionally computing system 11A and computing system 11B). Dataacquisition computer 8 transmits the fourth monitoring data to computingsystem 11.

Computing system 11 additionally retrieves fifth data (i.e., associatedwith the cohort) from sensors 10E located within aircraft 40B. The fifthdata was collected from sensors 10E while the cohort was located withinaircraft 40B (e.g., the cohort may have previously traveled on aircraft40B, landed at airport 15, and transferred to aircraft 40A).Additionally, external data from data retrieval/storage systems 5 istransmitted to computing system 11. The external data retrieved fromdata retrieval/storage systems 5 is associated with the cohort inaircraft 40A. The external data retrieved from data retrieval/storagesystems 5 may comprise any type of data associated with the cohortincluding, inter alia, individuals within the cohort that are on a nofly or watch list, ticket purchase information (e.g., payment method,timing of purchase, etc) associated with individuals within the cohort,a travel history of individuals in the cohort, etc. The first, second,third, fourth, and fifth monitoring data retrieved by data acquisitioncomputer 8 and the external data retrieved from data retrieval/storagesystems 5 is fed into inference engine 18. Inference engine 18 performsa massively recursive process in order to generate inferences based onthe first, second, third, fourth, and fifth monitoring data and theexternal data continuously being fed into inference engine 18 and rulesets 23 in computing system 11. The massively recursive processperformed by inference engine 18 (and optionally inference engine 18Awith respect to sensor data from sensors 10D and inference engine 18Bwith respect to sensor data from sensors 10E) is described by U.S.patent application Ser. No. 11/678,959 to Friedlander et al. (filed onFeb. 26, 2007), the disclosure of which is hereby incorporated herein byreference in its entirety. The process performed by inference engine 18is massively recursive in that every piece of information (e.g.,inferences, sensor data, etc) added to inference engine 18 causes theprocess to be re-executed. An entirely different outcome or result maybe generated based on new information inputted. Information may includethe fact that a query itself was simply made. Information may alsoinclude results of a query (i.e., feedback data) or information mayinclude data from any one of a number of sources. Inference engine 18generates the inferences using the following analytical process:

1. Record analytical data (i.e., data from sensors 10A, 10B, 10C, 10D,and 10E and data retrieval/storage systems 5) into inference engine 18.

2. Generate cohorts

3. Generate inferences and risk level scores.

4. Issue additional information queries (i.e., from sensors 10A, 10B,10C, 10D, and 10E and data retrieval/storage systems 5) based anomalouscohort behaviors.

5. Modify cohorts based on anomalous behavior.

6. Receive additional information.

7. Generate new inferences and risk level scores based anomalousbehaviors.

8. Continue cycling through process.

Several actions may be taken by computing system 11 based on theinferences generated by inference engine 18. The following listcomprises examples of actions that may be taken by computing system 11:

1. Generate informational alerts associated with a specified aircraft.

2. Generate warning alerts associated with a specified aircraft.

3. Generate actions to disrupt a potential situation. For example,seating plans on an aircraft may be modified, an aircraft may bedelayed, a security force may be dispatched, etc.

FIG. 2 illustrates a flowchart describing an algorithm used by system 2of FIG. 1 for performing a risk assessment process associated with airtravel, in accordance with embodiments of the present invention. In step202, a cohort (e.g., comprising a person(s)) enters a (1^(st)) aircraft40A (i.e., from a location external to aircraft 40A such as, inter alia,gate area 15B, a (2^(nd)) aircraft 40B, etc). In step 204, sensors 10Dare activated and monitoring is initialized. The cohort is continuouslymonitored while located within aircraft 40A. In step 207, data fromsensors 10D is transmitted (i.e., via data acquisition computer 8) tocomputing system 11 (and optionally computing system 11A) and sensorcohorts are generated. In step 209, group technology inferencesassociated with the cohort are generated (i.e., using sensor cohort dataand inference engine 18). In step 211, inference engine 18 generatesrisk cohort inferences based on the sensor (i.e., from sensors 10D)cohort data and the group technology inferences. In step 212, previouslygenerated inferences associated with the cohort(s) located withinsecurity perimeter area 12, pre/post security area 15A, gate area, andaircraft 40B are retrieved from inferences 27 in memory system 14. Instep 214, feedback inferences associated with the cohort and aircraft40A (i.e., previously generated inferences) are retrieved frominferences 27 to be used as input into inference engine 18. In step 216,any additional data (e.g., individuals within the cohort that are on ano fly or watch list, ticket purchase information (e.g., payment method,timing of purchase, etc) associated with individuals within the cohort,a travel history of individuals in the cohort, etc) and/or additionalinferences (e.g., generated by additional systems) are retrieved fromdata retrieval/storage systems 5. In step 218, inference enginegenerates new inferences based on all data and inferences generatedand/or retrieved in steps 207, 209, 211, 212, 214, and 216. In step 222,risk level scores for the cohort are generated based on the newinferences. In step 225, the new inferences and risk level scores arestored in memory system 14. In step 228, alerts are generated based onthe risk level scores. For example, if any of the risk level scoresexceed a predetermined threshold, an alert may be generated. In step232, responsive actions based on the alerts are generated. For example,responsive actions may comprise, inter alia, modifying seating plans onan aircraft, delaying an aircraft, dispatching a security force, etc. Instep 235, the risk level scores, alerts, and responsive actions aretransmitted to the proper authorities. Proper authorities may include,inter alia, police, military personnel, air traffic controller, aircraftpilots, airport personnel, etc. The massively recursive processdescribed in the algorithm of FIG. 2 is continuously repeated. Note thatall steps in FIG. 2 may be performed simultaneously.

FIG. 3 illustrates a flowchart describing an algorithm used by system 2of FIG. 1 for establishing a database adapted to establish a probabilityof an inference based on data contained in the database, in accordancewith embodiments of the present invention. The database established inthe algorithm of FIG. 3 is used to perform the process illustrated bythe algorithm of FIG. 2. In step 2100, the process begins as computingsystem 11 receives a database structure (i.e., comprising data retrievedin steps 207, 209, 212, 214, and 216 of FIG. 2). In step 2102, computingsystem 11, establishes a rules set for determining additional rule setsto be applied to a data query. The rules set is established in order tolimit a scope of comparison for a very large amount of data. Therefore,computing system 11 establishes a set of determination rules used todetermine the search rules used during a query. In step 2104, computingsystem 11 additionally receives divergent data. Divergent data isdefined as sets of data having different types, sizes, compatibilities,and other differences. Divergent data may be received from manydifferent sources. In step 2106, computing system 11 conforms receiveddivergent data to the database (e.g., memory system 14). In step 2108,computing system 11 stores conformed data and the process terminates instep 2110.

FIG. 4 illustrates a flowchart describing an algorithm used by system 2of FIG. 1 for executing of a query in a database to establish aprobability of an inference based on data contained in the database, inaccordance with embodiments of the present invention. The query executedin the algorithm of FIG. 3 is used to perform the process illustrated bythe algorithm of FIG. 2. In step 2200, computing system 11 receives aquery regarding a fact. In step 2202, computing system 11 establishesthe fact as a frame of reference for the query. In step 2204, computingsystem 11 determines a first set of rules for the query according to asecond set of rules. In step 2206, computing system 11 executes thequery according to the first set of rules to create a probability of aninference by comparing data in the database (e.g., memory system 14). Instep 2208, computing system 11 stores the probability of the firstinference and additionally stores the first inference. In step 2210,computing system 11 performs a recursion process. During the recursionprocess steps 2200 through 2208 are repeated again and again, as eachnew inference and each new probability becomes a new fact that can beused to generate a new probability and a new inference. Additionally,new facts may be received in memory system 14 during this process, andthose new facts also influence the resulting process. Each conclusion orinference generated during the recursion process may be presented to auser. As a first alternative, only a final conclusion or inference maybe presented to a user. As a second alternative, a number of conclusionsmade prior to step 2210 may be presented to a user.

In step 2212, computing system 11 determines whether the recursionprocess is complete. If in step 2212, computing system 11 determinesthat recursion is not complete, then the process illustrated in steps2200-2210 continues. If in step 2212, computing system 11 determinesthat recursion is complete then the process terminates in step 2214.

FIGS. 5A and 5B illustrate a flowchart describing an algorithm used bysystem 2 of FIG. 1 for executing a query in a database to establish aprobability of an inference based on data contained in the database, inaccordance with embodiments of the present invention. The query executedin the algorithm of FIGS. 5A and 5B is used to perform the processillustrated by the algorithm of FIG. 2. In step 2300, computing system11 receives an I^(th) query regarding an I^(th) fact. The term “I^(th)”refers to an integer, beginning with one. The integer reflects how manytimes a recursion process, referred to below, has been conducted. Thus,for example, when a query is first submitted that query is the 1^(st)query. The first recursion is the 2^(nd) query. The second recursion isthe 3^(rd) query, and so forth until recursion I−1 forms the “I^(th)”query. Similarly, but not the same, the I^(th) fact is the factassociated with the I^(th) query. Thus, the I^(th) fact is associatedwith the 1^(st) query, the 2^(nd) fact is associated with the 2^(nd)query, etc. The I^(th) fact can be the same as previous facts, such asthe I^(th)−1 fact, the I^(th)−2 fact, etc. The I^(th) fact can be acompound fact. A compound fact is a fact that includes multiplesub-facts. The I^(th) fact can start as a single fact and become acompound fact on subsequent recursions or iterations. The I^(th) fact islikely to become a compound fact during recursion, as additionalinformation is added to the central database during each recursion. Instep 2302, (i.e., after receiving the I^(th) query), computing system 11establishes the I^(th) fact as a frame of reference for the I^(th)query. A frame of reference is an anchor datum or set of data that isused to limit which data are searched in the database/memory system 14(i.e., defines a search space). The frame of reference also is used todetermine to what rules the searched data will be subject to. Therefore,when the query is executed, sufficient processing power will beavailable to make inferences. In step 2304, computing system 11determines an I^(th) set of rules using a J^(th) set of rules. In otherwords, a different set of rules is used to determine the set of rulesthat are actually applied to the I^(th) query. The term “J^(th)” refersto an integer, starting with one, wherein J=1 is the first iteration ofthe recursion process and I−1 is the J^(th) iteration of the recursionprocess. The J^(th) set of rules may or may not change from the previousset, such that J^(th)−1 set of rules may or may not be the same as theJ^(th) set of rules. The term “J^(th)” set of rules refers to the set ofrules that establishes the search rules, which are the I^(th) set ofrules. The J^(th) set of rules is used to determine the I^(th) set ofrules. In step 2306, computing system 11 determines an I^(th) searchspace). The I^(th) search space is the search space for the I^(th)iteration. A search space is the portion of a database (e.g., memorysystem 14), or a subset of data within a database, that is to besearched. In step 2308, computing system 11 prioritizes the I^(th) setof rules (i.e., determined during step 2304) in order to determine whichrules of the I^(th) set of rules should be executed first. Additionally,computing system 11 may prioritize the remaining rules in the I^(th) setof rules. Note that because computing resources are not infinite, thoserules that are most likely to produce useful or interesting results areexecuted first. In step 2310, computing system 11 executes the I^(th)query according to the I^(th) set of rules and within the I^(th) searchspace). As a result, in step 2312, computing system 11 creates an I^(th)probability of an I^(th) inference. An inference is a conclusion basedon a comparison of facts within the database (e.g., memory system 14).The probability of the inference is the likelihood that the inference istrue, or alternatively the probability that the inference is false. TheI^(th) probability and the I^(th) inference need not be the same as theprevious inference and probability in the recursion process, or onevalue could change but not the other. For example, as a result of therecursion process the I^(th) inference might be the same as the previousiteration in the recursion process, but the I^(th) probability couldincrease or decrease over the previous iteration in the recursionprocess. In contrast, the I^(th) inference may be completely differentthan the inference created in the previous iteration of the recursionprocess with a probability that is either the same or different than theprobability generated in the previous iteration of the recursionprocess. In step 2314, computing system 11 stores the I^(th) probabilityof the I^(th) inference as an additional datum in the database (e.g.,memory system 14). In step 2316, computing system 11 stores the I^(th)inference in the database (e.g., memory system 14). In step 2318,computing system 11 stores a categorization of the probability of theI^(th) inference in the database (e.g., memory system 14). In step 2320,computing system 11 stores the categorization of the I^(th) inference inthe database (e.g., memory system 14). In step 2322, computing system 11stores the rules that were triggered in the I^(th) set of rules togenerate the I^(th) inference. In step 2324, computing system 11 storesthe I^(th) search space. Additional information generated as a result ofexecuting the query may also be stored at this time. All of theinformation stored in steps 2314 through 2324, and possibly inadditional storage steps for additional information, may change howcomputing system 11 performs, how computing system 11 behaves, and maychange the result during each iteration.

The process then follows two paths simultaneously. First, computingsystem 11 performs a recursion process in step 2326 in which steps 2300through 2324 are continuously performed, as described above. Second,computing system 11 determines whether additional data is received instep 2330. Additionally, after each recursion, computing system 11determines whether the recursion is complete in step 2328. The processof recursion is complete when a threshold is met. As a first example, athreshold is a probability of an inference. When the probability of aninference decreases below a particular number, the recursion is completeand is made to stop. As a second example, a threshold is a number ofrecursions. Once the given number of recursions is met, the process ofrecursion stops. Other thresholds may also be used.

If the process of recursion is not complete, then recursion continues,beginning again with step 2300.

If the process of recursion is complete, then the process returns tostep 2330. Therefore, computing system 11 determines whether additionaldata is received at step 2330 during the recursion process in steps 2300through 2324 and after the recursion process is completed at step 2328.If additional data is received, then computing system 11 conforms theadditional data to the database (e.g., memory system 14) in step 2332.The system also associates metadata and a key with each additional datum(step 2334). A key uniquely identifies an individual datum. A key can beany unique identifier, such as a series of numbers, alphanumericcharacters, other characters, or other methods of uniquely identifyingobjects.

If computing system 11 determines that additional data has not beenreceived at step 2330, or after associating metadata and a key with eachadditional datum in step 2334, then computing system 11 determineswhether to modify the recursion process in step 2336. Modification ofthe recursion process may include determining new sets of rules,expanding the search space, performing additional recursions afterrecursions were completed at step 2328, or continuing the recursionprocess.

In response to a positive determination to modify the recursion processat step 2336, computing system 11 again repeats the determinationwhether additional data has been received at step 2330 and also performsadditional recursions from steps 2300 through 2324, as described withrespect to step 2326.

In step 2238, in response to a negative determination to modify therecursion process at step 2336, computing system 11 determines whetherto execute a new query. Computing system 11 may decide to execute a newquery based on an inference derived at step 2312, or may execute a newquery based on a prompt or entry by a user. If computing system 11executes a new query, then computing system may optionally continuerecursion at step 2326, begin a new query recursion process at step2300, or perform both simultaneously. Therefore, multiple queryrecursion processes may occur at a same time. However, if no new queryis to be executed at step 2338, then the process terminates in step2340.

FIG. 6 illustrates a computer apparatus 90 (e.g., computing system 11 ofFIG. 1) used for performing a risk assessment process associated withair travel, in accordance with embodiments of the present invention. Thecomputer system 90 comprises a processor 91, an input device 92 coupledto the processor 91, an output device 93 coupled to the processor 91,and memory devices 94 and 95 each coupled to the processor 91. The inputdevice 92 may be, inter alia, a keyboard, a mouse, etc. The outputdevice 93 may be, inter alia, a printer, a plotter, a computer screen, amagnetic tape, a removable hard disk, a floppy disk, etc. The memorydevices 94 and 95 may be, inter alia, a hard disk, a floppy disk, amagnetic tape, an optical storage such as a compact disc (CD) or adigital video disc (DVD), a dynamic random access memory (DRAM), aread-only memory (ROM), etc. The memory device 95 includes a computercode 97. The computer code 97 includes algorithms (e.g., the algorithmsof FIGS. 2-5B) for performing a risk assessment process associated withair travel. The processor 91 executes the computer code 97. The memorydevice 94 includes input data 96. The input data 96 includes inputrequired by the computer code 97. The output device 93 displays outputfrom the computer code 97. Either or both memory devices 94 and 95 (orone or more additional memory devices not shown in FIG. 6) may comprisethe algorithms of FIGS. 2-5B and may be used as a computer usable medium(or a computer readable medium or a program storage device) having acomputer readable program code embodied therein and/or having other datastored therein, wherein the computer readable program code comprises thecomputer code 97. Generally, a computer program product (or,alternatively, an article of manufacture) of the computer system 90 maycomprise said computer usable medium (or said program storage device).

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service provider who offers to perform a procurementrequisition process. Thus the present invention discloses a process fordeploying, creating, integrating, hosting, maintaining, and/orintegrating computing infrastructure, comprising integratingcomputer-readable code into the computer system 90, wherein the code incombination with the computer system 90 is capable of performing amethod for performing a risk assessment process associated with airtravel. In another embodiment, the invention provides a business methodthat performs the process steps of the invention on a subscription,advertising, and/or fee basis. That is, a service provider, such as aSolution Integrator, could offer to perform a risk assessment processassociated with air travel. In this case, the service provider cancreate, maintain, support, etc. a computer infrastructure that performsthe process steps of the invention for one or more customers. In return,the service provider can receive payment from the customer(s) under asubscription and/or fee agreement and/or the service provider canreceive payment from the sale of advertising content to one or morethird parties.

While FIG. 6 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 6. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

1. A risk assessment method comprising: receiving, by an inferenceengine within a computing system, first sensor cohort data associatedwith a first cohort, said first cohort located within a first aircraft;receiving, by said inference engine, first group technology inferencesassociated with said first cohort; generating, by said inference engine,first risk cohort inferences, said generating said first risk cohortinferences based on said first group technology inferences and saidfirst sensor cohort data; receiving, by said inference engine, firstinference data generated by said inference engine, said first inferencedata comprising a first plurality of inferences associated with saidfirst cohort and a security perimeter area surrounding an airport;receiving, by said inference engine, second inference data generated bysaid inference engine, said second inference data comprising a second ofplurality of inferences associated with said first cohort and a pre/postsecurity area within said airport; receiving, by said inference engine,third inference data generated by said inference engine, said thirdinference data comprising a third of plurality of inferences associatedwith said first cohort and a gate area within said airport; receiving,by said inference engine, fourth inference data generated by saidinference engine, said fourth inference data comprising a fourth ofplurality of inferences associated with said first cohort and a secondaircraft; receiving, by said inference engine, fifth inference datagenerated by said inference engine, said fifth inference data comprisinga fifth of plurality of inferences associated with said first cohort andsaid first aircraft; generating, by said inference engine, sixthinference data, said sixth inference data comprising a sixth pluralityof inferences associated with said first cohort and said first aircraft,wherein said generating said sixth inference data is based on said firstrisk cohort inferences, said first inference data, said second inferencedata, said third inference data, said fourth inference data, and saidfifth inference data; generating, by said inference engine based on saidsixth inference data, a first associated risk level score for said firstcohort; and storing, by said computing system, said sixth inference dataand said first associated risk level score.
 2. The method of claim 1,further comprising: receiving, by said inference engine, second sensorcohort data associated with a second cohort, said second cohort locatedwithin said first aircraft; receiving, by said inference engine, secondgroup technology inferences associated with said second cohort;generating, by said inference engine, second risk cohort inferences,said generating said second risk cohort inferences based on said secondgroup technology inferences and said second sensor cohort data;receiving, by said inference engine, seventh inference data generated bysaid inference engine, said seventh inference data comprising a seventhplurality of inferences associated with said second cohort and saidsecurity perimeter area surrounding said airport; receiving, by saidinference engine, eighth inference data generated by said inferenceengine, said eighth inference data comprising an eighth plurality ofinferences associated with said second cohort and said pre/post securityarea within said airport; receiving, by said inference engine, ninthinference data generated by said inference engine, said ninth inferencedata comprising a ninth plurality of inferences associated with saidsecond cohort and said gate area within said airport; receiving, by saidinference engine, tenth inference data generated by said inferenceengine, said tenth inference data comprising a tenth of plurality ofinferences associated with said second cohort and said second aircraft;generating, by said inference engine, eleventh inference data, saideleventh inference data comprising an eleventh plurality of inferencesassociated with said second cohort, wherein said generating saideleventh inference data is based on second risk cohort inferences, saidseventh inference data, said eighth inference data, said ninth inferencedata, and said tenth inference data; generating, by said inferenceengine based on said eleventh inference data, a second associated risklevel score for said second cohort; and storing, by said computingsystem, said eleventh inference data and said second associated risklevel score.
 3. The method of claim 2, further comprising: receiving, bysaid inference engine, twelfth inference data generated by saidinference engine, said twelfth inference data comprising a twelfthplurality of inferences associated with said second cohort and saidaircraft, wherein said generating said eleventh inference data isfurther based on said twelfth inference data.
 4. The method of claim 3,further comprising: generating, by said inference engine, thirteenthinference data, said thirteenth inference data comprising a thirteenthplurality of inferences associated with said second cohort, saidgenerating said thirteenth inference data based on said sixth inferencedata and said eleventh inference data; generating, by said inferenceengine based on said thirteenth inference data, a third associated risklevel score for said second cohort; and storing, by said computingsystem, said thirteenth inference data and said third associated risklevel score.
 5. The method of claim 4, further comprising: receiving, bysaid inference engine, third sensor cohort data associated with a thirdcohort, said third cohort located within an area governed by saidairport; receiving, by said inference engine, third group technologyinferences associated with said third cohort; generating, by saidinference engine, third risk cohort inferences, said generating saidthird risk cohort inferences based on said third group technologyinferences and said third sensor cohort data; generating, by saidinference engine, fourteenth inference data, said fourteenth inferencedata comprising a fourteenth plurality of inferences associated withsaid third cohort, wherein said generating said fourteenth inferencedata is based on said third risk cohort inferences and said thirteenthinference data; generating, by said inference engine based on saidfourteenth inference data, a fourth associated risk level score for saidthird cohort; and storing, by said computing system, said fourteenthinference data and said fourth associated risk level score.
 6. Themethod of claim 1, further comprising: presenting, by said computingsystem, said sixth inference data and said first associated risk levelscore.
 7. The method of claim 1, further comprising: comparing, by saidcomputing system, said first associated risk level score to apredetermined risk level threshold, wherein said comparing determinesthat said first associated risk level score exceeds said predeterminedrisk level threshold; generating, by said computing system, an alertassociated with said first associated risk level score exceeding saidpredetermined risk level threshold; and presenting by said computingsystem, said alert.
 8. The method of claim 7, further comprising:generating, by said computing system in response to said alert,responsive actions associated with said alert and said first cohort. 9.The method of claim 7, further comprising: transmitting, by saidcomputing system, said alert to said second aircraft.
 10. The method ofclaim 7, further comprising: modifying, by said computing system inresponse to said alert, a transponder code for a transponder associatedwith said first aircraft.
 11. The method of claim 1, wherein said firstsensor cohort data comprises data selected from the group consisting ofaudio sensor data, video sensor data, biometrical sensor data, olfactorysensor data, and sensory/actuator data.
 12. The method of claim 1,wherein said first sensor cohort data comprises data associated with acohort selected from the group consisting of a retina detection cohort,a directional viewing cohort, a respiration cohort, a perspirationcohort, a pallor cohort, and a facial recognition cohort.
 13. A processfor supporting computer infrastructure, said process comprisingproviding at least one support service for at least one of creating,integrating, hosting, maintaining, and deploying computer-readable codein a computing system, wherein the code in combination with thecomputing system is capable of performing the method of claim
 1. 14. Acomputer program product, comprising a computer storage mediumcomprising a computer readable program code embodied therein, saidcomputer readable program code configured to perform the method of claim1 upon being executed by a processor of said computing system.
 15. Themethod of claim 1, wherein said computing system comprises a systemselected from the group consisting of a services oriented architecturesystem and an independent Web 2.0 application/mashup system.
 16. Themethod of claim 1, further comprising: receiving, by said inferenceengine, additional inference data, said additional inference datacomprising an additional plurality of inferences associated with saidfirst cohort; generating, by said inference engine based on saidadditional inference data and said sixth inference data, a secondassociated risk level score for said first cohort; and storing, by saidcomputing system, said second associated risk level score.
 17. Themethod of claim 1, further comprising: receiving, by an additionalinference engine within an additional computing system located withinsaid first aircraft, said first sensor cohort data; receiving, by saidadditional inference engine, said first group technology inferencesassociated with said first cohort; generating, by said additionalinference engine, second risk cohort inferences, said generating saidsecond risk cohort inferences based on said first group technologyinferences and said first sensor cohort data; receiving, by saidadditional inference engine, feedback inference data generated by saidadditional inference engine, said feedback inference data comprisingfeedback inferences associated with said first cohort and said firstaircraft; generating, by said additional inference engine, additionalinference data, said additional inference data comprising an additionalplurality of inferences associated with said first cohort and said firstaircraft, wherein said generating said additional inference data isbased on said second risk cohort inferences, said additional inferencedata, and said feedback inference data; generating, by said additionalinference engine based on said additional inference data, a secondassociated risk level score for said first cohort; storing, by saidadditional computing system, said additional inference data and saidsecond associated risk level score; and modifying, by said additionalcomputing system in response to said second associated risk level score,a transponder code for a transponder associated with said firstaircraft.
 18. A computing system comprising a processor coupled to acomputer-readable memory unit, said memory unit comprising instructionsthat when executed by the processor implement a risk assessment method,said method comprising: receiving, by an inference engine within saidcomputing system, first sensor cohort data associated with a firstcohort, said first cohort located within a first aircraft; receiving, bysaid inference engine, first group technology inferences associated withsaid first cohort; generating, by said inference engine, first riskcohort inferences, said generating said first risk cohort inferencesbased on said first group technology inferences and said first sensorcohort data; receiving, by said inference engine, first inference datagenerated by said inference engine, said first inference data comprisinga first plurality of inferences associated with said first cohort and asecurity perimeter area surrounding an airport; receiving, by saidinference engine, second inference data generated by said inferenceengine, said second inference data comprising a second of plurality ofinferences associated with said first cohort and a pre/post securityarea within said airport; receiving, by said inference engine, thirdinference data generated by said inference engine, said third inferencedata comprising a third of plurality of inferences associated with saidfirst cohort and a gate area within said airport; receiving, by saidinference engine, fourth inference data generated by said inferenceengine, said fourth inference data comprising a fourth of plurality ofinferences associated with said first cohort and a second aircraft;receiving, by said inference engine, fifth inference data generated bysaid inference engine, said fifth inference data comprising a fifth ofplurality of inferences associated with said first cohort and firstaircraft; generating, by said inference engine, sixth inference data,said sixth inference data comprising a sixth plurality of inferencesassociated with said first cohort and said first aircraft, wherein saidgenerating said sixth inference data is based on said first risk cohortinferences, said first inference data, said second inference data, saidthird inference data, said fourth inference data, and said fifthinference data; generating, by said inference engine based on said sixthinference data, a first associated risk level score for said firstcohort; and storing, by said computing system, said sixth inference dataand said first associated risk level score.
 19. The computing system ofclaim 18, wherein said method further comprises: receiving, by saidinference engine, second sensor cohort data associated with a secondcohort, said second cohort located within said first aircraft;receiving, by said inference engine, second group technology inferencesassociated with said second cohort; generating, by said inferenceengine, second risk cohort inferences, said generating said second riskcohort inferences based on said second group technology inferences andsaid second sensor cohort data; receiving, by said inference engine,seventh inference data generated by said inference engine, said seventhinference data comprising a seventh plurality of inferences associatedwith said second cohort and said security perimeter area surroundingsaid airport; receiving, by said inference engine, eighth inference datagenerated by said inference engine, said eighth inference datacomprising an eighth plurality of inferences associated with said secondcohort and said pre/post security area within said airport; receiving,by said inference engine, ninth inference data generated by saidinference engine, said ninth inference data comprising a ninth pluralityof inferences associated with said second cohort and said gate areawithin said airport; receiving, by said inference engine, tenthinference data generated by said inference engine, said tenth inferencedata comprising a tenth of plurality of inferences associated with saidsecond cohort and said second aircraft; generating, by said inferenceengine, eleventh inference data, said eleventh inference data comprisingan eleventh plurality of inferences associated with said second cohort,wherein said generating said eleventh inference data is based on secondrisk cohort inferences, said seventh inference data, said eighthinference data, said ninth inference data, and said tenth inferencedata; generating, by said inference engine based on said eleventhinference data, a second associated risk level score for said secondcohort; and storing, by said computing system, said eleventh inferencedata and said second associated risk level score.
 20. The computingsystem of claim 19, wherein said method further comprises: receiving, bysaid inference engine, twelfth inference data generated by saidinference engine, said twelfth inference data comprising a twelfthplurality of inferences associated with said second cohort and saidaircraft, wherein said generating said eleventh inference data isfurther based on said twelfth inference data.
 21. The computing systemof claim 20, wherein said method further comprises: generating, by saidinference engine, thirteenth inference data, said, said thirteenthinference data comprising a thirteenth plurality of inferencesassociated with said second cohort, said generating said thirteenthinference data based on said sixth inference data and said eleventhinference data; generating, by said inference engine based on saidthirteenth inference data, a third associated risk level score for saidsecond cohort; and storing, by said computing system, said thirteenthinference data and said third associated risk level score.
 22. Thecomputing system of claim 21, wherein said method further comprises:receiving, by said inference engine, third sensor cohort data associatedwith a third cohort, said third cohort located within an area governedby said airport; receiving, by said inference engine, third grouptechnology inferences associated with said third cohort; generating, bysaid inference engine, third risk cohort inferences, said generatingsaid third risk cohort inferences based on said third group technologyinferences and said third sensor cohort data; generating, by saidinference engine, fourteenth inference data, said fourteenth inferencedata comprising a fourteenth plurality of inferences associated withsaid third cohort, wherein said generating said fourteenth inferencedata is based on said third risk cohort inferences and said thirteenthinference data; generating, by said inference engine based on saidfourteenth inference data, a fourth associated risk level score for saidthird cohort; and storing, by said computing system, said fourteenthinference data and said fourth associated risk level score.
 23. Thecomputing system of claim 18, wherein said method further comprises:presenting, by said computing system, said sixth inference data and saidfirst associated risk level score.
 24. The computing system of claim 18,wherein said method further comprises: comparing, by said computingsystem, said first associated risk level score to a predetermined risklevel threshold, wherein said comparing determines that said firstassociated risk level score exceeds said predetermined risk levelthreshold; generating, by said computing system, an alert associatedwith said first associated risk level score exceeding said predeterminedrisk level threshold; and presenting by said computing system, saidalert.
 25. The computing system of claim 24, wherein said method furthercomprises: generating, by said computing system in response to saidalert, responsive actions associated with said alert and said firstcohort.